Continual And Reinforcement Learning For Edge Ai
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Continual And Reinforcement Learning For Edge Ai
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Author : Hang Wang
language : en
Publisher: Springer Nature
Release Date : 2025-05-20
Continual And Reinforcement Learning For Edge Ai written by Hang Wang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-20 with Computers categories.
This book provides a comprehensive introduction to continual and reinforcement learning for edge AI, which investigates how to build an AI agent that can continuously solve new learning tasks and enhance the AI at resource-limited edge devices. The authors introduce readers to practical frameworks and in-depth algorithmic foundations. The book surveys the recent advances in the area, coming from both academic researchers and industry professionals. The authors also present their own research findings on continual and reinforcement learning for edge AI. The book also covers the practical applications of the topic and identifies exciting future research opportunities.
Reinforcement Learning
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Author : Richard S. Sutton
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Reinforcement Learning written by Richard S. Sutton and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Computers categories.
Reinforcement learning is the learning of a mapping from situations to actions so as to maximize a scalar reward or reinforcement signal. The learner is not told which action to take, as in most forms of machine learning, but instead must discover which actions yield the highest reward by trying them. In the most interesting and challenging cases, actions may affect not only the immediate reward, but also the next situation, and through that all subsequent rewards. These two characteristics -- trial-and-error search and delayed reward -- are the most important distinguishing features of reinforcement learning. Reinforcement learning is both a new and a very old topic in AI. The term appears to have been coined by Minsk (1961), and independently in control theory by Walz and Fu (1965). The earliest machine learning research now viewed as directly relevant was Samuel's (1959) checker player, which used temporal-difference learning to manage delayed reward much as it is used today. Of course learning and reinforcement have been studied in psychology for almost a century, and that work has had a very strong impact on the AI/engineering work. One could in fact consider all of reinforcement learning to be simply the reverse engineering of certain psychological learning processes (e.g. operant conditioning and secondary reinforcement). Reinforcement Learning is an edited volume of original research, comprising seven invited contributions by leading researchers.
Edge Ai Merging Iot And Machine Learning For Real Time Analytics
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Author : Dr. D. Srinivasa Rao
language : en
Publisher: Xoffencer International Book Publication house
Release Date : 2024-10-10
Edge Ai Merging Iot And Machine Learning For Real Time Analytics written by Dr. D. Srinivasa Rao and has been published by Xoffencer International Book Publication house this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-10 with Computers categories.
In order to provide real-time analytics directly at the edge of the network, edge artificial intelligence (AI) is a disruptive technique that combines the capabilities of Internet of Things (IoT) devices with the power of machine learning (ML). As a result of this paradigm shift away from conventional cloud-centric approaches, latency is reduced, privacy is improved, and operational efficiency is increased. Information is processed locally on devices. The Internet of Things (IoT) is experiencing exponential expansion, which presents a problem for centralized cloud processing due to the sheer amount of data created by sensors, cameras, and linked equipment of all kinds. By putting artificial intelligence closer to the source of the data, Edge AI makes it possible to make decisions more quickly and reduces the need for continual data transmission to the cloud, which in turn reduces the expenses associated with bandwidth and cloud storage. Innovation is fostered across a variety of sectors, including healthcare, smart cities, autonomous cars, and industrial automation, via the integration of the Internet of Things (IoT) and machine learning at the edge. Real-time analytics makes it possible to identify trends and irregularities, which in turn leads to improvements in accessibility and efficiency in areas such as tailored services, increased security, and predictive maintenance. Utilizing on-device machine learning models enables quick insights, which is essential in applications that are time-sensitive. This is also true as Internet of Things devices grow more sophisticated. Furthermore, the infrastructure for edge computing is capable of supporting dispersed systems, which not only ensures increased system resilience but also reduces the likelihood of downtime. Nevertheless, putting Edge AI into practice is not without its difficulties. The management of the computational needs of machine learning models on devices with limited resources, the maintenance of scalability, and the guarantee of security across dispersed nodes are all key concerns that need to be addressed. The development of lightweight machine learning models, hardware that has been optimized, and security mechanisms that have been improved are all essential components in promoting the widespread use of this technology. Furthermore, the continuing developments in 5G networks and edge computing frameworks promise to push the frontiers of edge artificial intelligence, which will offer up new opportunities for real-time, decentralized intelligence. In conclusion, Edge AI is able to bridge the gap between the increasing needs of Internet of Things ecosystems and the requirement for real-time insights that can be put into action. With the ability to facilitate decision-making processes that are quicker, more intelligent, and more secure, it has the potential to completely transform whole sectors. Artificial intelligence at the edge of the network will play a crucial part in determining the future of intelligent systems as technology continues to advance
Edge Ai For Industry 5 0 And Healthcare 5 0 Applications
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Author : Pethuru Raj
language : en
Publisher: CRC Press
Release Date : 2025-04-04
Edge Ai For Industry 5 0 And Healthcare 5 0 Applications written by Pethuru Raj and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-04 with Computers categories.
Edge AI is the seamless and spontaneous combination of Edge or Fog computing and AI. It enables acquiring real-time insights, which, in turn, leads to the realization of real-time, people-centric, event-driven, business-critical, process-aware, and knowledge-filled software services and applications. Edge AI for Industry 5.0 and Healthcare 5.0 Applications looks at the unique contributions of Edge AI for developing solutions for Industry 5.0 and Healthcare 5.0. It explains how Industry 5.0 fine tunes the human-machine connection and leverages tiny, high-performance AI-centric processors in IoT edge devices for real-time decision-making and application processing. Focusing on Explainable AI (XAI), the book discusses: · The role of XAI in Healthcare 5.0 · Best practices, challenges, and opportunities of applying XAI in healthcare setting · How to enhance transparency and trust of XAI in Healthcare 5.0 · XAI and its methods in predicting healthcare outcomes Other highlights of the book include: · 5G communication networks requirements · The fusion of IoT, AI, Edge, Cloud, and blockchain · Trustworthiness of blockchain technology in healthcare 5.0 and Industry 5.0 · The future of trust and the potential of blockchain technology By explaining how Edge AI can transform healthcare and industry, this book empowers researchers and professionals to envisage and implement sophisticated and smart digital solutions.
Mastering Machine Learning
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Author : Cybellium
language : en
Publisher: Cybellium Ltd
Release Date : 2023-09-05
Mastering Machine Learning written by Cybellium and has been published by Cybellium Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-05 with Computers categories.
Are you ready to become a master of machine learning? In "Mastering Machine Learning" by Kris Hermans, you'll embark on a transformative journey that will empower you with the skills and knowledge needed to conquer the world of data-driven intelligence. Discover Cutting-Edge Techniques and Practical Applications From self-driving cars to personalized recommendations, machine learning is transforming industries and reshaping the way we live and work. In this comprehensive guide, Kris Hermans equips you with the tools to harness the power of machine learning. Dive into the core concepts, algorithms, and models that underpin this revolutionary field. Become a Proficient Practitioner Whether you're a beginner or an experienced professional, this book provides a clear and structured path to mastering machine learning. Through hands-on examples and real-world case studies, you'll gain practical expertise in implementing machine learning models and solving complex problems. Kris Hermans guides you through the process, ensuring you develop a deep understanding of the techniques and algorithms that drive intelligent systems. From Fundamentals to Advanced Topics "Mastering Machine Learning" covers the full spectrum of machine learning, starting with the foundations of supervised and unsupervised learning and progressing to reinforcement learning, neural networks, and deep learning. Explore diverse models and learn how to choose the right approach for different applications. With this knowledge, you'll be able to tackle real-world challenges with confidence. Unlock the Potential of Machine Learning Across Industries Discover how machine learning is revolutionizing industries such as finance, healthcare, e-commerce, and cybersecurity. Through captivating case studies, you'll witness the transformative impact of machine learning and gain insights into how organizations are leveraging this technology to drive innovation, improve decision-making, and achieve unprecedented success. Navigate Ethical Considerations As machine learning becomes increasingly powerful, it's crucial to consider the ethical implications. "Mastering Machine Learning" addresses these important considerations head-on. Learn about the ethical challenges and responsibilities associated with machine learning applications and gain the knowledge to make informed, ethical decisions in your own work.
Deep Learning
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Author : Rob Botwright
language : en
Publisher: Rob Botwright
Release Date : 2024
Deep Learning written by Rob Botwright and has been published by Rob Botwright this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024 with Computers categories.
Introducing the Ultimate AI Book Bundle: Deep Learning, Computer Vision, Python Machine Learning, and Neural Networks Are you ready to embark on an exhilarating journey into the world of artificial intelligence, deep learning, and computer vision? Look no further! Our carefully curated book bundle, "DEEP LEARNING: COMPUTER VISION, PYTHON MACHINE LEARNING AND NEURAL NETWORKS," offers you a comprehensive roadmap to AI mastery. BOOK 1 - DEEP LEARNING DEMYSTIFIED: A BEGINNER'S GUIDE 🚀 Perfect for beginners, this book dismantles the complexities of deep learning. From neural networks to Python programming, you'll build a strong foundation in AI. BOOK 2 - MASTERING COMPUTER VISION WITH DEEP LEARNING 🌟 Dive into the captivating world of computer vision. Unlock the secrets of image processing, convolutional neural networks (CNNs), and object recognition. Harness the power of visual intelligence! BOOK 3 - PYTHON MACHINE LEARNING AND NEURAL NETWORKS: FROM NOVICE TO PRO 📊 Elevate your skills with this intermediate volume. Delve into data preprocessing, supervised and unsupervised learning, and become proficient in training neural networks. BOOK 4 - ADVANCED DEEP LEARNING: CUTTING-EDGE TECHNIQUES AND APPLICATIONS 🔥 Ready to conquer advanced techniques? Learn optimization strategies, tackle common deep learning challenges, and explore real-world applications shaping the future. 🎉 What You'll Gain: · A strong foundation in deep learning · Proficiency in computer vision · Mastery of Python machine learning · Advanced deep learning skills · Real-world application knowledge · Cutting-edge AI insights 📚 Why Choose Our Book Bundle? · Expertly curated content · Beginner to expert progression · Clear explanations and hands-on examples · Comprehensive coverage of AI topics · Practical real-world applications · Stay ahead with emerging AI trends 🌐 Who Should Grab This Bundle? · Beginners eager to start their AI journey · Intermediate learners looking to expand their skill set · Experts seeking advanced deep learning insights · Anyone curious about AI's limitless possibilities 📦 Limited-Time Offer: Get all four books in one bundle and save! Don't miss this chance to accelerate your AI knowledge and skills. 🔒 Secure Your AI Mastery: Click "Add to Cart" now and embark on an educational adventure that will redefine your understanding of artificial intelligence. Your journey to AI excellence begins here!
Ethical Considerations And Bias Detection In Artificial Intelligence Machine Learning Applications
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Author : Jayesh Rane
language : en
Publisher: Deep Science Publishing
Release Date : 2025-07-10
Ethical Considerations And Bias Detection In Artificial Intelligence Machine Learning Applications written by Jayesh Rane and has been published by Deep Science Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-10 with Computers categories.
At a time when artificial intelligence (AI) and machine learning (ML) are used to make sensitive societal decisions such as the ones related to criminal justice, healthcare, finance, education, employment, algorithmic fairness and bias mitigation are among the most important but challenging issues at hand. The goal of this book is to provide a holistic view across various disciplines of the ethical base, detection methods, and technical measures for trustworthy AI systems. Starting from a solid foundation of statistical bias, transparency systems and fairness-aware ML models, this book methodically looks at state-of-the-art methodologies, where we highlight their shortcomings and introduce a unified model framework for detecting bias and transparent algorithms. Moving beyond technical diagnoses, it examines key sociotechnical and policy tools that are required to implement AI responsibly, providing guidance to researchers, engineers, policy makers, and organizational leaders. Literature review has been driven following the experimental case, the fairness trade-offs, intersectional bias, explainability and regulatory compliance are discussed in depth by the authors. This work underscores that fairness in automated decision-making systems depends not only on algorithmic accuracy, but also institutional will and stakeholder engagement. The chapters in this book function as both an academic primer and a resourceful handbook, transitioning readers through an ever-growing ethical AI terrain. Whether you are a data scientist building and deploying an algorithm that encourages ethical speech, or a regulator working to create and refine guidelines around such algorithms, this book provides you with both the tools and the understanding you need for ethical technology development and deployment.
Cloud Based Machine Learning Practical Guide To Deploying Ai Models In The Cloud
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Author : Hemanth Volikatla
language : en
Publisher: RK Publication
Release Date : 2024-05-15
Cloud Based Machine Learning Practical Guide To Deploying Ai Models In The Cloud written by Hemanth Volikatla and has been published by RK Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-15 with Computers categories.
Cloud-Based Machine Learning – Practical Guide to Deploying AI Models in the Cloud is a comprehensive resource designed to help professionals and enthusiasts harness the power of cloud platforms for AI deployment. It's key concepts, tools, and techniques for building, training, and deploying machine learning models using services like AWS, Azure, and Google Cloud. With practical examples, step-by-step instructions, and best practices, this guide empowers readers to scale AI solutions efficiently, ensuring robust performance and seamless integration into real-world applications. Perfect for beginners and experts aiming to advance their skills in cloud-based AI technologies.
Artificial Intelligence Mastering Automation With Ai In 2025
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Author : A. Adams
language : en
Publisher: Code Academy
Release Date : 2025-05-06
Artificial Intelligence Mastering Automation With Ai In 2025 written by A. Adams and has been published by Code Academy this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-06 with Computers categories.
Unlock the power of Artificial Intelligence with Artificial Intelligence: Mastering Automation with AI in 2025. This comprehensive guide takes you on a practical journey through AI fundamentals, automation techniques, real-world applications, and the latest trends shaping our future. Whether you're a beginner or a tech enthusiast, this book will help you understand how AI is transforming industries, from smart assistants to intelligent systems. With easy-to-follow explanations, hands-on insights, and forward-looking strategies, you'll be equipped to thrive in the AI-driven world of 2025.
Applied Edge Ai
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Author : Pethuru Raj
language : en
Publisher: CRC Press
Release Date : 2022-04-05
Applied Edge Ai written by Pethuru Raj and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-04-05 with Computers categories.
The strategically sound combination of edge computing and artificial intelligence (AI) results in a series of distinct innovations and disruptions enabling worldwide enterprises to visualize and realize next-generation software products, solutions and services. Businesses, individuals, and innovators are all set to embrace and experience the sophisticated capabilities of Edge AI. With the faster maturity and stability of Edge AI technologies and tools, the world is destined to have a dazzling array of edge-native, people-centric, event-driven, real-time, service-oriented, process-aware, and insights-filled services. Further on, business workloads and IT services will become competent and cognitive with state-of-the-art Edge AI infrastructure modules, AI algorithms and models, enabling frameworks, integrated platforms, accelerators, high-performance processors, etc. The Edge AI paradigm will help enterprises evolve into real-time and intelligent digital organizations. Applied Edge AI: Concepts, Platforms, and Industry Use Cases focuses on the technologies, processes, systems, and applications that are driving this evolution. It examines the implementation technologies; the products, processes, platforms, patterns, and practices; and use cases. AI-enabled chips are exclusively used in edge devices to accelerate intelligent processing at the edge. This book examines AI toolkits and platforms for facilitating edge intelligence. It also covers chips, algorithms, and tools to implement Edge AI, as well as use cases. FEATURES The opportunities and benefits of intelligent edge computing Edge architecture and infrastructure AI-enhanced analytics in an edge environment Encryption for securing information An Edge AI system programmed with Tiny Machine learning algorithms for decision making An improved edge paradigm for addressing the big data movement in IoT implementations by integrating AI and caching to the edge Ambient intelligence in healthcare services and in development of consumer electronic systems Smart manufacturing of unmanned aerial vehicles (UAVs) AI, edge computing, and blockchain in systems for environmental protection Case studies presenting the potential of leveraging AI in 5G wireless communication